Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification

2017/01/01 by Xavier Roynard, Roynard, Xavier, Jean‐Emmanuel Deschaud +3 · 12 citations
Environmental Science · Earth and Planetary Sciences · Engineering · #Remote Sensing and LiDAR Applications #3D Surveying and Cultural Heritage #3D Shape Modeling and Analysis

paper · pdf · doi:10.48550/arxiv.1712.00032

Abstract

This paper introduces a new Urban Point Cloud Dataset for Automatic\nSegmentation and Classification acquired by Mobile Laser Scanning (MLS). We\ndescribe how the dataset is obtained from acquisition to post-processing and\nlabeling. This dataset can be used to learn classification algorithm, however,\ngiven that a great attention has been paid to the split between the different\nobjects, this dataset can also be used to learn the segmentation. The dataset\nconsists of around 2km of MLS point cloud acquired in two cities. The number of\npoints and range of classes make us consider that it can be used to train\nDeep-Learning methods. Besides we show some results of automatic segmentation\nand classification. The dataset is available at:\nhttp://caor-mines-paristech.fr/fr/paris-lille-3d-dataset/\n

Citations

• University of Michigan North Campus long-term vision and lidar dataset
• 1 year, 1000 km: The Oxford RobotCar dataset

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• Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges
• Deep Learning for 3D Point Clouds: A Survey
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• WHU-STree: A Multi-modal Benchmark Dataset for Street Tree Inventory
• Review: deep learning on 3D point clouds
• Deep Learning for 3D Point Clouds: A Survey
• Simulated LiDAR Repositioning: a novel point cloud data augmentation\n method
• Few-Shot Point Cloud Region Annotation with Human in the Loop

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